Association genetics for earliness components and QTL-based ecophysiological predictions of heading date provide tools to optimize heading date through breeding
Wheat Breeding Assembly
Data Scientist / Digital Agronomist
a research scientist of digital agriculture at the CSIRO.
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Identification of Earliness Per Se Flowering Time Locus in Spring Wheat through a Genome-Wide Association Study
Gene-based prediction of heading time to target real-time and future climate adaptation in wheat
Predicting heading time of Australian wheat using effects of VRN1 and Ppd-D1
Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments
Using gene-based information to adapt wheat flowering time to avoid heat, frost and drought stresses in current and future climates